As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial. In order to demonstrate the trustworthiness of an AS, we first need to specify what is required for an...
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Hybrid meta-heuristics algorithms have gained popularity in recent years to solve t-way test suite generation problems due to better exploration and exploitation capabilities of the hybridization. This paper presents ...
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The heart disease is also known as coronary artery disease, many hearts affecting symptoms that are very common nowadays and causes death. It is a challenging task to diagnose heart diseases without any intelligent di...
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Robotic technology holds a significant role within the realm of smart industries, wherein all functionalities are executed within real-time systems. The verification of robot operations is a crucial aspect in the cont...
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Agriculture is very important to the prosperity of the country. Climate change has created a variety of problems for the agricultural academic system. Since machine learning (ML) provides effective and affordable solu...
Agriculture is very important to the prosperity of the country. Climate change has created a variety of problems for the agricultural academic system. Since machine learning (ML) provides effective and affordable solutions, it is the most effective method for solving problems. Crop yield prediction involves estimating the crop yield based on historical data and a variety of other factors, including the climate, sometimes the environment, the water source, and warmth. This study analyses and discusses the use of the linear regression technique for evaluating agricultural production based on data from the earlier study. This study uses geographic and meteorological data to anticipate winter wheat production. It is motivated by this data science issue. The project’s goal is to find a solution to the cost loss issue. Actual agricultural data is used to build the models, which are then evaluated on samples. Using the vegetable yield forecasting model, end users (agriculturalist) may foresee crop production as they plant crops on agricultural land. The Linear Regression Algorithm method is used to forecast results with accuracy. The decision-making model’s creation will be aided by the availability of a sizable dataset.
Facial emotion recognition (FER) plays a critical role in understanding human behavior, especially for individuals suffering from neurological disorders (ND) like Parkinson's Disease (PD), Multiple Sclerosis (MS),...
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Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrain...
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The increasing need for the examination of evidence from mobile and portable gadgets increases the essential need to establish dependable measures for the investigation of these gadgets. Many differences exist while d...
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Smart wearables and body implanted IoT devices continuously track and transmit health metrics wirelessly to a central controller, such as a personal server, enabling real-time monitoring, proactive treatment and timel...
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ISBN:
(数字)9798331534103
ISBN:
(纸本)9798331534110
Smart wearables and body implanted IoT devices continuously track and transmit health metrics wirelessly to a central controller, such as a personal server, enabling real-time monitoring, proactive treatment and timely assistance. However, the security of IoT-enabled healthcare systems remains a concern, particularly regarding the protection of sensitive patient data. To address the aforementioned issue and enhance security in IoT-enabled healthcare services, we propose a robust anonymous authentication scheme in this work. By leveraging lightweight cryptographic primitives including hash functions and XOR operations coupled with physical unclonable functions (PUFs) and fuzzy extractors, the proposed scheme introduces a novel authentication and key agreement mechanism for secure communication in IoT-enabled healthcare systems. The integration of PUF technology ensure the physical security of resource constrained implantable medical devices (IMDs) against various attacks including device physical capture and impersonation attacks that compromise the IoT-enabled healthcare operations. A thorough security analysis demonstrated the robustness and resilience of the proposed scheme against various active and pas-sive security attacks, thus ensure the integrity and confidentiality of sensitive healthcare data. Finally, comparative analysis of the proposed scheme with other state-of-the-art highlighted that our scheme outperformed other approaches in terms of providing security and additional features, positioning it as a comprehensive solution for authentication in IoT-enabled healthcare services.
Ahstract- Timely detection of stroke is paramount for effective intervention and improved patient outcomes. This study introduces an innovative method for early stroke prediction utilizing machine learning (ML) models...
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ISBN:
(数字)9798350349450
ISBN:
(纸本)9798350349467
Ahstract- Timely detection of stroke is paramount for effective intervention and improved patient outcomes. This study introduces an innovative method for early stroke prediction utilizing machine learning (ML) models optimized through Grid-Search Optimization and elucidated using Shapley Additive Explanations (SHAP). The model not only identifies at-risk individuals but also enables timely preventative interventions. Twelve ML models were thoroughly compared and evaluated with the XGBoost model appearing as the superior performance across all metrics, achieving an accuracy of 94.29%, precision of 92.65%, recall of 96.19%, F1-score of 94.39%, and an impressive AUC ROC value of 99.82%. Furthermore, the SHAP analysis further enhances interpretability, highlighting elevated glucose as a major risk factor, along with age and BMI, aligning with established medical knowledge. This integrated ML methodology, combining performance-driven grid-search optimization and interpretability through SHAP, holds promise for assisting healthcare professionals in targeted stroke prevention strategies by accurately identifying high-risk individuals.
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